Cross‐Cultural Measurement Invariance of Adolescent Self‐Report on the Pediatric Quality of Life Inventory™ 4.0

This study evaluated the cross‐cultural measurement invariance of the Pediatric Quality of Life Inventory version 4.0 (PedsQL^?) among adolescents sampled from Bulgaria, Croatia, India, Indonesia, Nigeria, Serbia, and Turkey. The multiple‐indicator multiple‐cause (MIMIC) model was used, which allowed controlling of demographic variables (i.e., age, gender, and socioeconomic status). Significant effects of country on scores within the PedsQL^? domains were observed, with up to 17 items showing differential item functioning (DIF) across the countries. We did not find support for cross‐cultural measurement invariance hypotheses for scores on the PedsQL^? adolescent self‐report in this study. Researchers should use caution in making cross‐cultural quality of life comparisons while using the PedsQL.     

Macroeconomic consequences of terrorism in Pakistan

The objective of the study examines the macroeconomic consequences of terrorism in Pakistan. The study evaluates the short- and long-run relationship between terrorism and economic factors over a period of 1975–2011. Both objectives have been achieved with the sophisticated econometrics techniques including cointegration theory, Granger causality test and variance decomposition, etc. The result reveals that macroeconomic factors, i.e., population growth, price level, poverty and political instability cause the terrorism incidence in Pakistan. However, income inequality, unemployment and trade openness have no long-run relationship with the terrorism incidence in Pakistan. The study may conclude that, for some how, Pakistan's macroeconomic indicators have significant long-run equilibrium with terrorism incidence. The result of Granger causality indicates that except unemployment, all other macroeconomic indicators have unidirectional causality with terrorism incidence. Unemployment has a bi-directional causality with the terrorism incidence in Pakistan. The results of variance decomposition indicate that there exists statistically significant cointegration among macroeconomic factors and terrorism incidence in Pakistan. Among macroeconomic factors, changes in price level exert the largest influence on terrorism in Pakistan. Contrary, the influence of poverty seems relatively the least contribution level for changes in terrorism incidence in Pakistan.     

Nonparametric estimation of the entropy using a ranked set sample

This article is concerned with nonparametric estimation of the entropy in ranked set sampling. Theoretical properties of the proposed estimator are studied. The proposed estimator is compared with the rival estimator in simple random sampling. The applications of the proposed estimator to the mutual information estimation as well as estimation of the Kullback–Leibler divergence are provided. Several Monté-Carlo simulation studies are conducted to examine the performance of the estimator. The results are applied to the longleaf pine (Pinus palustris) trees and the body fat percentage datasets to illustrate applicability of theoretical results.     

A General Approach for Preservation of Some Aging Classes Under Weighting

In this article, we apply univariate characterizations of some monotonic aging classes to investigate the preservation problem of those classes and stochastic orders under weighting. The results are also examined and discussed for special cases of the weighted distributions, as well.     

Timing the Use of Breast Cancer Risk Information in Biopsy Decision‐Making

Available clinical evidence is inconclusive on whether radiologists should use the patient risk profile information when interpreting mammograms. On the one hand, risk profile information is informative and can improve radiologists’ performance, but on the other hand, it may impair their judgment by introducing biases in mammography interpretation. Therefore, it is important to assess whether and when profile information use translates into improved outcomes. We model the use of profile information in mammography, using a decision theoretic approach and explore the value of profile information using three process design choices: mammography only, unbiased, and biased reading. We estimate the parameters of our model using clinical data and find that using profile information along with the mammography information can achieve a better performance than not using the profile information. However, the better performance is contingent on the weight assigned to the profile information as well as the extent of bias due to profile information. Translating our findings into clinical practice would require properly designed experiments aiming to quantify the effect of the timing and the use of profile information on performance while accounting for radiologist and patient characteristics. When conducting an experiment is not feasible, a uniform operational sequence for interpreting mammograms and related guidelines may be a useful starting point to improve the quality of mammography operations.     

Random effect exponentiated-exponential geometric model for clustered/longitudinal zero-inflated count data

For count responses, there are situations in biomedical and sociological applications in which extra zeroes occur. Modeling correlated (e.g. repeated measures and clustered) zero-inflated count data includes special challenges because the correlation between measurements for a subject or a cluster needs to be taken into account. Moreover, zero-inflated count data are often faced with over/under dispersion problem. In this paper, we propose a random effect model for repeated measurements or clustered data with over/under dispersed response called random effect zero-inflated exponentiated-exponential geometric regression model. The proposed method was illustrated through real examples. The performance of the model and asymptotical properties of the estimations were investigated using simulation studies.KEYWORDS: Count model, under- and over-dispersion, zero-inflation, mixture model, zero-inflated poisson model